It’s all a numbers game for Purdue student Jeremy Frank from his major in data science to his passion for baseball statistics, as captured in the popular @MLBRandomStats, which he runs on Twitter. Frank hopes to continue his passion beyond college by working in baseball, possibly for a major league team or as a sports statistician.
Monthly Archives: May 2020
Purdue Data Science Student Delves into America’s Pastime through His Love of Statistics
Research Highlights: MIDAS – Real-time Anomaly/Fake News/Intrusion Detection
In the insideBIGDATA Research Highlights column we take a look at new and upcoming results from the research community for data science, machine learning, AI and deep learning. Our readers need to get a glimpse for technology coming down the pipeline that will make their efforts more strategic and competitive. In this installment we review MIDAS – Real-time Anomaly/Fake News/Intrusion Detection developed by Ph.D. candidate Siddharth Bhatia and his team at the National University of Singapore.
Lucidworks Announces Advanced Linguistics Package to Improve Search Precision for Global Companies Serving Asian, European, and Middle Eastern Markets
Lucidworks, leader in AI-powered search, announced the Advanced Linguistics Package for Lucidworks Fusion to power personalized search for users in Asian, European, and Middle Eastern markets. Lucidworks now embeds text analytics from Basis Technology, a leading provider of AI for natural language processing. With the Advanced Linguistics Package, global organizations that support multiple languages can make the information and insights they manage more accessible, more relevant and more personalized for their global audience.
We’re on a road to economic recovery. IBM’s Cloud Pak for Data helps Wunderman Thompson build guideposts for this journey
As communities and businesses worldwide look to understand the economic impact of COVID-19 and prepare for an eventual recovery, the biggest test of decision-making will be the data that will inform the business decisions. Was it trusted? Was it timely? Was it enough?
To date, there are many efforts to release COVID-19 dashboards that can give us a hint on what to do next. They include health conditions, COVID-19 cases, death rates and demographics, sure, but no source has been able to deliver data on risk, readiness and recovery—until now.
Four critical data management attributes for AI and digital transformation
Many enterprises have a tangled data management system, comprised of an assortment of products assembled together, in an attempt to meet the complex needs of modern day data management. The labyrinth of convoluted data management systems often evolves as a natural response to data growth, diversity of data types, and varying needs based on business objectives.
Matillion Launches Matillion ETL for Azure Synapse Empowering Users with Data Transformation Capabilities for Rapid Access to Insights
Matillion, a leading provider of data transformation software for cloud data warehouses (CDWs), announced the availability of Matillion ETL for Azure Synapse to enable data transformations in complex IT environments, at scale. Empowering enterprises to achieve faster time to insights by loading, transforming, and joining together data, the release extends Matillion’s product portfolio to further serve Microsoft Azure customers.
What happened to Netezza?
There are some people, like me, who like to know how the story ends and thus may occasionally read the last chapter before going back and reading the rest of the book. So, I guess this is a spoiler alert. The answer to the question is, “Netezza is still alive, well and evolving and IBM has now come out with the next generation of Netezza as part of Cloud Pak for Data System.”
The Rise of No-code Knowledge Graphs
In this contributed article, Marta V. Lopata, Chief Growth Officer at Kgbase, discusses the use of knowledge graphs. With a no-code approach, they bring the best of the data science world to medicine, finance, business, education and the arts enabling anyone to generate and visualize unique insights from siloed data sources.
Monte Carlo Method and Price Testing: Old Solution for Modern Problems
In this special guest feature, Vladimir Kuchkanov, Pricing Solution Architect at Competera, examines how data scientists often forget about classics while good old algorithms are still relevant and efficient. In particular, Monte Carlo method (MCM) pops up in mind. Among many fields of its application, MCM has established itself as a solid solution in price prediction and automation of pricing rules in retail. Like any analytical approach, MCM has limitations and inaccuracies. Despite this, many fields, including retail, still utilize it.
Five reasons to master your product data
Accurate product details and descriptions shape buyer and user behavior. These details – product size, weights, descriptions, reviews, certifications, etc – make the products findable to address shopper needs and allow for comparison. The need extends across industries, including both traditional products one could buy in a store, and less tangible products and services, such as credit cards, loans or cell phone plans that one could find in the financial services or communications industries.
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